






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase								=		logging.get_logger(__name__)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    YolosConfig:
							_lowercase							:   List[Any] 							=   YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														_lowercase							:   List[str] 							=   192
														_lowercase							:   Optional[int] 							=   768
														_lowercase							:   int 							=   12
														_lowercase							:   Union[str, Any] 							=   3
														_lowercase							:   Dict 							=   [800, 1_333]
														_lowercase							:   Any 							=   False
							elif yolos_name == "yolos_s_dWr":
														_lowercase							:   Any 							=   330
														_lowercase							:   Dict 							=   14
														_lowercase							:   Optional[Any] 							=   6
														_lowercase							:   List[str] 							=   1_320
							elif "yolos_s" in yolos_name:
														_lowercase							:   Union[str, Any] 							=   384
														_lowercase							:   Optional[Any] 							=   1_536
														_lowercase							:   Tuple 							=   12
														_lowercase							:   Tuple 							=   6
							elif "yolos_b" in yolos_name:
														_lowercase							:   Optional[int] 							=   [800, 1_344]

							_lowercase							:   int 							=   91
							_lowercase							:   Union[str, Any] 							=   'huggingface/label-files'
							_lowercase							:   List[Any] 							=   'coco-detection-id2label.json'
							_lowercase							:   Optional[int] 							=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     repo_type='dataset'				)     ,     'r'				)				)
							_lowercase							:   Optional[int] 							=   {int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
							_lowercase							:   Union[str, Any] 							=   idalabel
							_lowercase							:   Optional[int] 							=   {v: k for k, v in idalabel.items()}

							return config






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = False				)     ->    str:
							for i in range(config.num_hidden_layers				):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														_lowercase							:   str 							=   state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
														_lowercase							:   Dict 							=   state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
														# next, add query, keys and values (in that order) to the state dict
														_lowercase							:   int 							=   in_proj_weight[: config.hidden_size, :]
														_lowercase							:   Union[str, Any] 							=   in_proj_bias[: config.hidden_size]
														_lowercase							:   Any 							=   in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														_lowercase							:   Union[str, Any] 							=   in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														_lowercase							:   Tuple 							=   in_proj_weight[-config.hidden_size :, :]
														_lowercase							:   Tuple 							=   in_proj_bias[-config.hidden_size :]






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    str:
							if "backbone" in name:
														_lowercase							:   Tuple 							=   name.replace('backbone'     ,     'vit'				)
							if "cls_token" in name:
														_lowercase							:   Optional[int] 							=   name.replace('cls_token'     ,     'embeddings.cls_token'				)
							if "det_token" in name:
														_lowercase							:   List[Any] 							=   name.replace('det_token'     ,     'embeddings.detection_tokens'				)
							if "mid_pos_embed" in name:
														_lowercase							:   Optional[int] 							=   name.replace('mid_pos_embed'     ,     'encoder.mid_position_embeddings'				)
							if "pos_embed" in name:
														_lowercase							:   Optional[int] 							=   name.replace('pos_embed'     ,     'embeddings.position_embeddings'				)
							if "patch_embed.proj" in name:
														_lowercase							:   Optional[Any] 							=   name.replace('patch_embed.proj'     ,     'embeddings.patch_embeddings.projection'				)
							if "blocks" in name:
														_lowercase							:   List[str] 							=   name.replace('blocks'     ,     'encoder.layer'				)
							if "attn.proj" in name:
														_lowercase							:   Union[str, Any] 							=   name.replace('attn.proj'     ,     'attention.output.dense'				)
							if "attn" in name:
														_lowercase							:   Any 							=   name.replace('attn'     ,     'attention.self'				)
							if "norm1" in name:
														_lowercase							:   Tuple 							=   name.replace('norm1'     ,     'layernorm_before'				)
							if "norm2" in name:
														_lowercase							:   Tuple 							=   name.replace('norm2'     ,     'layernorm_after'				)
							if "mlp.fc1" in name:
														_lowercase							:   Any 							=   name.replace('mlp.fc1'     ,     'intermediate.dense'				)
							if "mlp.fc2" in name:
														_lowercase							:   List[str] 							=   name.replace('mlp.fc2'     ,     'output.dense'				)
							if "class_embed" in name:
														_lowercase							:   Optional[Any] 							=   name.replace('class_embed'     ,     'class_labels_classifier'				)
							if "bbox_embed" in name:
														_lowercase							:   Dict 							=   name.replace('bbox_embed'     ,     'bbox_predictor'				)
							if "vit.norm" in name:
														_lowercase							:   List[Any] 							=   name.replace('vit.norm'     ,     'vit.layernorm'				)

							return name






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    dict:
							for key in orig_state_dict.copy().keys():
														_lowercase							:   List[str] 							=   orig_state_dict.pop(SCREAMING_SNAKE_CASE				)

														if "qkv" in key:
																					_lowercase							:   List[str] 							=   key.split('.'				)
																					_lowercase							:   Optional[Any] 							=   int(key_split[2]				)
																					_lowercase							:   Tuple 							=   model.vit.encoder.layer[layer_num].attention.attention.all_head_size
																					if "weight" in key:
																												_lowercase							:   Union[str, Any] 							=   val[:dim, :]
																												_lowercase							:   Optional[int] 							=   val[
																												    dim : dim * 2, :
																												]
																												_lowercase							:   Dict 							=   val[-dim:, :]
																					else:
																												_lowercase							:   Optional[int] 							=   val[:dim]
																												_lowercase							:   str 							=   val[dim : dim * 2]
																												_lowercase							:   Union[str, Any] 							=   val[-dim:]
														else:
																					_lowercase							:   Tuple 							=   val

							return orig_state_dict






def    __magic_name__    (	)     ->    torch.Tensor:
							_lowercase							:   Any 							=   'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowercase							:   Any 							=   Image.open(requests.get(SCREAMING_SNAKE_CASE     ,     stream=SCREAMING_SNAKE_CASE				).raw				)
							return im






@torch.no_grad()
def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = False				)     ->    Any:
							_lowercase							:   Dict 							=   get_yolos_config(SCREAMING_SNAKE_CASE				)

							# load original state_dict
							_lowercase							:   Optional[int] 							=   torch.load(SCREAMING_SNAKE_CASE     ,     map_location='cpu'				)['model']

							# load 🤗 model
							_lowercase							:   Optional[int] 							=   YolosForObjectDetection(SCREAMING_SNAKE_CASE				)
							model.eval()
							_lowercase							:   int 							=   convert_state_dict(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							model.load_state_dict(SCREAMING_SNAKE_CASE				)

							# Check outputs on an image, prepared by YolosImageProcessor
							_lowercase							:   Optional[Any] 							=   800 if yolos_name != 'yolos_ti' else 512
							_lowercase							:   List[str] 							=   YolosImageProcessor(format='coco_detection'     ,     size=SCREAMING_SNAKE_CASE				)
							_lowercase							:   Union[str, Any] 							=   image_processor(images=prepare_img()     ,     return_tensors='pt'				)
							_lowercase							:   List[Any] 							=   model(**SCREAMING_SNAKE_CASE				)
							_lowercase							,							_lowercase							:   Tuple 							=   outputs.logits, outputs.pred_boxes

							_lowercase							,							_lowercase							:   Union[str, Any] 							=   None, None
							if yolos_name == "yolos_ti":
														_lowercase							:   List[Any] 							=   torch.tensor(
														    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
														_lowercase							:   Tuple 							=   torch.tensor(
														    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]				)
							elif yolos_name == "yolos_s_200_pre":
														_lowercase							:   List[str] 							=   torch.tensor(
														    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
														_lowercase							:   Union[str, Any] 							=   torch.tensor(
														    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]				)
							elif yolos_name == "yolos_s_300_pre":
														_lowercase							:   Optional[Any] 							=   torch.tensor(
														    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
														_lowercase							:   List[Any] 							=   torch.tensor(
														    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]				)
							elif yolos_name == "yolos_s_dWr":
														_lowercase							:   Any 							=   torch.tensor(
														    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
														_lowercase							:   Any 							=   torch.tensor(
														    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]				)
							elif yolos_name == "yolos_base":
														_lowercase							:   Optional[int] 							=   torch.tensor(
														    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
														_lowercase							:   Dict 							=   torch.tensor(
														    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]				)
							else:
														raise ValueError(F"""Unknown yolos_name: {yolos_name}"""				)

							assert torch.allclose(logits[0, :3, :3]     ,     SCREAMING_SNAKE_CASE     ,     atol=1E-4				)
							assert torch.allclose(pred_boxes[0, :3, :3]     ,     SCREAMING_SNAKE_CASE     ,     atol=1E-4				)

							Path(SCREAMING_SNAKE_CASE				).mkdir(exist_ok=SCREAMING_SNAKE_CASE				)
							print(F"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(SCREAMING_SNAKE_CASE				)
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE				)

							if push_to_hub:
														_lowercase							:   Union[str, Any] 							=   {
														    'yolos_ti': 'yolos-tiny',
														    'yolos_s_200_pre': 'yolos-small',
														    'yolos_s_300_pre': 'yolos-small-300',
														    'yolos_s_dWr': 'yolos-small-dwr',
														    'yolos_base': 'yolos-base',
														}

														print('Pushing to the hub...'				)
														_lowercase							:   str 							=   model_mapping[yolos_name]
														image_processor.push_to_hub(SCREAMING_SNAKE_CASE     ,     organization='hustvl'				)
														model.push_to_hub(SCREAMING_SNAKE_CASE     ,     organization='hustvl'				)


if __name__ == "__main__":
							UpperCamelCase								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--yolos_name",
							    default="yolos_s_200_pre",
							    type=str,
							    help=(
							        "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
							        " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
							    ),
							)
							parser.add_argument(
							    "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)

							UpperCamelCase								=		parser.parse_args()
							convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch



@require_torch
@require_optimum
@slow
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  tokenizer('''This is me'''      ,return_tensors='''pt'''			)

				lowerCAmelCase												=  model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				lowerCAmelCase												=  model.generate(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

					self.assertFalse(
					    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

					lowerCAmelCase												=  model_reloaded.generate(**SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  '''hf-internal-testing/tiny-random-t5'''
				lowerCAmelCase												=  AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
						model.save_pretrained(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  model.reverse_bettertransformer()
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


